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main_ddpir_sisr.py
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main_ddpir_sisr.py
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import os.path
import cv2
import logging
import numpy as np
import torch
import torch.nn.functional as F
from datetime import datetime
from collections import OrderedDict
import hdf5storage
from utils import utils_model
from utils import utils_logger
from utils import utils_sisr as sr
from utils import utils_image as util
from utils.utils_resizer import Resizer
from functools import partial
# from guided_diffusion import dist_util
from guided_diffusion.script_util import (
NUM_CLASSES,
model_and_diffusion_defaults,
create_model_and_diffusion,
args_to_dict,
)
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
noise_level_img = 12.75/255.0 # set AWGN noise level for LR image, default: 0
noise_level_model = noise_level_img # set noise level of model, default: 0
model_name = 'diffusion_ffhq_10m' # diffusion_ffhq_10m, 256x256_diffusion_uncond; set diffusino model
testset_name = 'demo_test' # set testing set, 'imagenet_val' | 'ffhq_val'
num_train_timesteps = 1000
iter_num = 100 # set number of sampling iterations
iter_num_U = 1 # set number of inner iterations, default: 1
skip = num_train_timesteps//iter_num # skip interval
sr_mode = 'blur' # 'blur', 'cubic' mode of sr up/down sampling
show_img = False # default: False
save_L = True # save LR image
save_E = False # save estimated image
save_LEH = False # save zoomed LR, E and H images
save_progressive = True # save generation process
sigma = max(0.001,noise_level_img) # noise level associated with condition y
lambda_ = 1. # key parameter lambda
sub_1_analytic = True # use analytical solution
log_process = False
ddim_sample = False # sampling method
model_output_type = 'pred_xstart' # model output type: pred_x_prev; pred_xstart; epsilon; score
generate_mode = 'DiffPIR' # DiffPIR; DPS; vanilla
skip_type = 'quad' # uniform, quad
eta = 0. # eta for ddim sampling
zeta = 0.1
guidance_scale = 1.0
test_sf = [4] # set scale factor, default: [2, 3, 4], [2], [3], [4]
inIter = 1 # iter num for sr solution: 4-6
gamma = 1/100 # coef for iterative sr solver 20steps: 0.05-0.10 for zeta=1, 0.09-0.13 for zeta=0
classical_degradation = False # set classical degradation or bicubic degradation
task_current = 'sr' # 'sr' for super resolution
n_channels = 3 # fixed
cwd = ''
model_zoo = os.path.join(cwd, 'model_zoo') # fixed
testsets = os.path.join(cwd, 'testsets') # fixed
results = os.path.join(cwd, 'results') # fixed
result_name = f'{testset_name}_{task_current}_{generate_mode}_{sr_mode}{str(test_sf)}_{model_name}_sigma{noise_level_img}_NFE{iter_num}_eta{eta}_zeta{zeta}_lambda{lambda_}'
model_path = os.path.join(model_zoo, model_name+'.pt')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.cuda.empty_cache()
calc_LPIPS = True
# noise schedule
beta_start = 0.1 / 1000
beta_end = 20 / 1000
betas = np.linspace(beta_start, beta_end, num_train_timesteps, dtype=np.float32)
betas = torch.from_numpy(betas).to(device)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas.cpu(), axis=0)
sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
sqrt_1m_alphas_cumprod = torch.sqrt(1. - alphas_cumprod)
reduced_alpha_cumprod = torch.div(sqrt_1m_alphas_cumprod, sqrt_alphas_cumprod) # equivalent noise sigma on image
noise_model_t = utils_model.find_nearest(reduced_alpha_cumprod, 2 * noise_level_model)
noise_model_t = 0
noise_inti_img = 50 / 255
t_start = utils_model.find_nearest(reduced_alpha_cumprod, 2 * noise_inti_img) # start timestep of the diffusion process
t_start = num_train_timesteps - 1
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
logger_name = result_name
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
# ----------------------------------------
# load model
# ----------------------------------------
model_config = dict(
model_path=model_path,
num_channels=128,
num_res_blocks=1,
attention_resolutions="16",
) if model_name == 'diffusion_ffhq_10m' \
else dict(
model_path=model_path,
num_channels=256,
num_res_blocks=2,
attention_resolutions="8,16,32",
)
args = utils_model.create_argparser(model_config).parse_args([])
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys()))
# model.load_state_dict(
# dist_util.load_state_dict(args.model_path, map_location="cpu")
# )
model.load_state_dict(torch.load(args.model_path, map_location="cpu"))
model.eval()
if generate_mode != 'DPS_y0':
# for DPS_yt, we can avoid backward through the model
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('model_name:{}, sr_mode:{}, image sigma:{:.3f}, model sigma:{:.3f}'.format(model_name, sr_mode, noise_level_img, noise_level_model))
logger.info('eta:{:.3f}, zeta:{:.3f}, lambda:{:.3f}, guidance_scale:{:.2f} '.format(eta, zeta, lambda_, guidance_scale))
logger.info('start step:{}, skip_type:{}, skip interval:{}, skipstep analytic steps:{}'.format(t_start, skip_type, skip, noise_model_t))
logger.info('analytic iter num:{}, gamma:{}'.format(inIter, gamma))
logger.info('Model path: {:s}'.format(model_path))
logger.info(L_path)
L_paths = util.get_image_paths(L_path)
# --------------------------------
# load kernel
# --------------------------------
# kernels = hdf5storage.loadmat(os.path.join('kernels', 'Levin09.mat'))['kernels']
if classical_degradation:
kernels = hdf5storage.loadmat(os.path.join(cwd, 'kernels', 'kernels_12.mat'))['kernels']
else:
kernels = hdf5storage.loadmat(os.path.join(cwd, 'kernels', 'kernels_bicubicx234.mat'))['kernels']
test_results_ave = OrderedDict()
test_results_ave['psnr_sf_k'] = []
test_results_ave['psnr_y_sf_k'] = []
if calc_LPIPS:
import lpips
loss_fn_vgg = lpips.LPIPS(net='vgg').to(device)
test_results_ave['lpips'] = []
for sf in test_sf:
border = sf
k_num = 8 if classical_degradation else 1
for k_index in range(k_num):
logger.info('--------- sf:{:>1d} --k:{:>2d} ---------'.format(sf, k_index))
if not classical_degradation: # for bicubic degradation
k_index = sf-2 if sf < 5 else 2
k = kernels[0, k_index].astype(np.float64)
util.surf(k) if show_img else None
def test_rho(lambda_=lambda_, zeta=zeta, model_output_type=model_output_type):
logger.info('eta:{:.3f}, zeta:{:.3f}, lambda:{:.3f}, inIter:{:.3f}, gamma:{:.3f}, guidance_scale:{:.2f}'.format(eta, zeta, lambda_, inIter, gamma, guidance_scale))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['psnr_y'] = []
if calc_LPIPS:
test_results['lpips'] = []
for idx, img in enumerate(L_paths):
model_out_type = model_output_type
# --------------------------------
# (1) get img_L
# --------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
img_H = util.imread_uint(img, n_channels=n_channels)
img_H = util.modcrop(img_H, sf) # modcrop
if sr_mode == 'blur':
if classical_degradation:
img_L = sr.classical_degradation(img_H, k, sf)
util.imshow(img_L) if show_img else None
img_L = util.uint2single(img_L)
else:
img_L = util.imresize_np(util.uint2single(img_H), 1/sf)
elif sr_mode == 'cubic':
img_H_tensor = np.transpose(img_H, (2, 0, 1))
img_H_tensor = torch.from_numpy(img_H_tensor)[None,:,:,:].to(device)
img_H_tensor = img_H_tensor / 255
# set up resizers
up_sample = partial(F.interpolate, scale_factor=sf)
down_sample = Resizer(img_H_tensor.shape, 1/sf).to(device)
img_L = down_sample(img_H_tensor)
img_L = img_L.cpu().numpy() #[0,1]
img_L = np.squeeze(img_L)
if img_L.ndim == 3:
img_L = np.transpose(img_L, (1, 2, 0))
np.random.seed(seed=0) # for reproducibility
img_L = img_L * 2 - 1
img_L += np.random.normal(0, noise_level_img * 2, img_L.shape) # add AWGN
img_L = img_L / 2 + 0.5
# --------------------------------
# (2) get rhos and sigmas
# --------------------------------
sigmas = []
sigma_ks = []
rhos = []
for i in range(num_train_timesteps):
sigmas.append(reduced_alpha_cumprod[num_train_timesteps-1-i])
if model_out_type == 'pred_xstart' and generate_mode == 'DiffPIR':
sigma_ks.append((sqrt_1m_alphas_cumprod[i]/sqrt_alphas_cumprod[i]))
#elif model_out_type == 'pred_x_prev':
else:
sigma_ks.append(torch.sqrt(betas[i]/alphas[i]))
rhos.append(lambda_*(sigma**2)/(sigma_ks[i]**2))
rhos, sigmas, sigma_ks = torch.tensor(rhos).to(device), torch.tensor(sigmas).to(device), torch.tensor(sigma_ks).to(device)
# --------------------------------
# (3) initialize x, and pre-calculation
# --------------------------------
x = cv2.resize(img_L, (img_L.shape[1]*sf, img_L.shape[0]*sf), interpolation=cv2.INTER_CUBIC)
if np.ndim(x)==2:
x = x[..., None]
if classical_degradation:
x = sr.shift_pixel(x, sf)
x = util.single2tensor4(x).to(device)
y = util.single2tensor4(img_L).to(device) #(1,3,256,256) [0,1]
# x = torch.randn_like(x)
x = sqrt_alphas_cumprod[t_start] * (2*x-1) + sqrt_1m_alphas_cumprod[t_start] * torch.randn_like(x)
k_tensor = util.single2tensor4(np.expand_dims(k, 2)).to(device)
FB, FBC, F2B, FBFy = sr.pre_calculate(y, k_tensor, sf)
# --------------------------------
# (4) main iterations
# --------------------------------
progress_img = []
# create sequence of timestep for sampling
skip = num_train_timesteps//iter_num
if skip_type == 'uniform':
seq = [i*skip for i in range(iter_num)]
if skip > 1:
seq.append(num_train_timesteps-1)
elif skip_type == "quad":
seq = np.sqrt(np.linspace(0, num_train_timesteps**2, iter_num))
seq = [int(s) for s in list(seq)]
seq[-1] = seq[-1] - 1
progress_seq = seq[::max(len(seq)//10,1)]
if progress_seq[-1] != seq[-1]:
progress_seq.append(seq[-1])
# reverse diffusion for one image from random noise
for i in range(len(seq)):
curr_sigma = sigmas[seq[i]].cpu().numpy()
# time step associated with the noise level sigmas[i]
t_i = utils_model.find_nearest(reduced_alpha_cumprod,curr_sigma)
# skip iters
if t_i > t_start:
continue
# repeat for semantic consistence: from repaint
for u in range(iter_num_U):
# --------------------------------
# step 1, reverse diffsuion step
# --------------------------------
### solve equation 6b with one reverse diffusion step
if 'DPS' in generate_mode:
x = x.requires_grad_()
xt, x0 = utils_model.model_fn(x, noise_level=curr_sigma*255, model_out_type='pred_x_prev_and_start', \
model_diffusion=model, diffusion=diffusion, ddim_sample=ddim_sample, alphas_cumprod=alphas_cumprod)
else:
x0 = utils_model.model_fn(x, noise_level=curr_sigma*255, model_out_type=model_out_type, \
model_diffusion=model, diffusion=diffusion, ddim_sample=ddim_sample, alphas_cumprod=alphas_cumprod)
# x0 = utils_model.test_mode(utils_model.model_fn, model, x, mode=2, refield=32, min_size=256, modulo=16, noise_level=curr_sigma*255, \
# model_out_type=model_out_type, diffusion=diffusion, ddim_sample=ddim_sample, alphas_cumprod=alphas_cumprod)
# --------------------------------
# step 2, FFT
# --------------------------------
if seq[i] != seq[-1]:
if generate_mode == 'DiffPIR':
if sub_1_analytic:
if model_out_type == 'pred_xstart':
# when noise level less than given image noise, skip
if i < num_train_timesteps-noise_model_t:
if sr_mode == 'blur':
tau = rhos[t_i].float().repeat(1, 1, 1, 1)
x0_p = x0 / 2 + 0.5
x0_p = sr.data_solution(x0_p.float(), FB, FBC, F2B, FBFy, tau, sf)
x0_p = x0_p * 2 - 1
# effective x0
x0 = x0 + guidance_scale * (x0_p-x0)
elif sr_mode == 'cubic':
# iterative back-projection (IBP) solution
for _ in range(inIter):
x0 = x0 / 2 + 0.5
x0 = x0 + gamma * up_sample((y - down_sample(x0))) / (1+rhos[t_i])
x0 = x0 * 2 - 1
else:
model_out_type = 'pred_x_prev'
x0 = utils_model.model_fn(x, noise_level=curr_sigma*255,model_out_type=model_out_type, \
model_diffusion=model, diffusion=diffusion, ddim_sample=ddim_sample, alphas_cumprod=alphas_cumprod)
# x0 = utils_model.test_mode(utils_model.model_fn, model, x, mode=2, refield=32, min_size=256, modulo=16, noise_level=curr_sigma*255, \
# model_out_type=model_out_type, diffusion=diffusion, ddim_sample=ddim_sample, alphas_cumprod=alphas_cumprod)
pass
else:
# zeta=0.25; lambda_=15: FFHQ
# zeta=0.35; lambda_=35: ImageNet
x0 = x0.requires_grad_()
# first order solver
down_sample = Resizer(x.shape, 1/sf).to(device)
#norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=x0/2+0.5, x_hat=x0, measurement=y)
norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=x0, x_hat=x0, measurement=2*y-1)
x0 = x0 - norm_grad * norm / (rhos[t_i])
x0 = x0.detach_()
pass
elif 'DPS' in generate_mode:
down_sample = Resizer(x.shape, 1/sf).to(device)
if generate_mode == 'DPS_y0':
norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=x, x_hat=x0, measurement=2*y-1)
#norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=xt, x_hat=x0, measurement=2*y-1) # does not work
x = xt - norm_grad * 1. #norm / (2*rhos[t_i])
x = x.detach_()
pass
elif generate_mode == 'DPS_yt':
y_t = sqrt_alphas_cumprod[t_i] * (2*y-1) + sqrt_1m_alphas_cumprod[t_i] * torch.randn_like(y) # add AWGN
#y_t = y_t/2 + 0.5
#norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=x, x_hat=xt, measurement=y_t) # no need to use
norm_grad, norm = utils_model.grad_and_value(operator=down_sample,x=xt, x_hat=xt, measurement=y_t)
x = xt - norm_grad * lambda_ * norm / (rhos[t_i]) * 0.35
x = x.detach_()
pass
# add noise back to t=i-1
if (generate_mode == 'DiffPIR' and model_out_type == 'pred_xstart') and not (seq[i] == seq[-1] and u == iter_num_U-1):
#x = sqrt_alphas_cumprod[t_i] * (x0) + (sqrt_1m_alphas_cumprod[t_i]) * torch.randn_like(x)
t_im1 = utils_model.find_nearest(reduced_alpha_cumprod,sigmas[seq[i+1]].cpu().numpy())
eps = (x - sqrt_alphas_cumprod[t_i] * x0) / sqrt_1m_alphas_cumprod[t_i]
# calculate \hat{\eposilon}
eta_sigma = eta * sqrt_1m_alphas_cumprod[t_im1] / sqrt_1m_alphas_cumprod[t_i] * torch.sqrt(betas[t_i])
x = sqrt_alphas_cumprod[t_im1] * x0 + np.sqrt(1-zeta) * (torch.sqrt(sqrt_1m_alphas_cumprod[t_im1]**2 - eta_sigma**2) * eps \
+ eta_sigma * torch.randn_like(x)) + np.sqrt(zeta) * sqrt_1m_alphas_cumprod[t_im1] * torch.randn_like(x)
else:
#x = x0
pass
# set back to x_t from x_{t-1}
if u < iter_num_U-1 and seq[i] != seq[-1]:
### it's equivalent to use x & xt (?), but with xt the computation is faster.
# x = torch.sqrt(alphas[t_i]) * x + torch.sqrt(betas[t_i]) * torch.randn_like(x)
sqrt_alpha_effective = sqrt_alphas_cumprod[t_i] / sqrt_alphas_cumprod[t_im1]
x = sqrt_alpha_effective * x + torch.sqrt(sqrt_1m_alphas_cumprod[t_i]**2 - \
sqrt_alpha_effective**2 * sqrt_1m_alphas_cumprod[t_im1]**2) * torch.randn_like(x)
# save the process
x_0 = (x/2+0.5)
if save_progressive and (seq[i] in progress_seq):
x_show = x_0.clone().detach().cpu().numpy() #[0,1]
x_show = np.squeeze(x_show)
if x_show.ndim == 3:
x_show = np.transpose(x_show, (1, 2, 0))
progress_img.append(x_show)
if log_process:
logger.info('{:>4d}, steps: {:>4d}, np.max(x_show): {:.4f}, np.min(x_show): {:.4f}'.format(seq[i], t_i, np.max(x_show), np.min(x_show)))
if show_img:
util.imshow(x_show)
# --------------------------------
# (3) img_E
# --------------------------------
img_E = util.tensor2uint(x_0)
psnr = util.calculate_psnr(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
if calc_LPIPS:
img_H_tensor = np.transpose(img_H, (2, 0, 1))
img_H_tensor = torch.from_numpy(img_H_tensor)[None,:,:,:].to(device)
img_H_tensor = img_H_tensor / 255 * 2 -1
lpips_score = loss_fn_vgg(x_0.detach()*2-1, img_H_tensor)
lpips_score = lpips_score.cpu().detach().numpy()[0][0][0][0]
test_results['lpips'].append(lpips_score)
logger.info('{:->4d}--> {:>10s} -- sf:{:>1d} --k:{:>2d} PSNR: {:.4f}dB LPIPS: {:.4f} ave LPIPS: {:.4f}'.format(idx+1, img_name+ext, sf, k_index, psnr, lpips_score, sum(test_results['lpips']) / len(test_results['lpips'])))
else:
logger.info('{:->4d}--> {:>10s} -- sf:{:>1d} --k:{:>2d} PSNR: {:.4f}dB'.format(idx+1, img_name+ext, sf, k_index, psnr))
if save_E:
util.imsave(img_E, os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index)+'_'+model_name+ext))
if n_channels == 1:
img_H = img_H.squeeze()
if save_progressive:
now = datetime.now()
current_time = now.strftime("%Y_%m_%d_%H_%M_%S")
img_total = cv2.hconcat(progress_img)
if show_img:
util.imshow(img_total,figsize=(80,4))
util.imsave(img_total*255., os.path.join(E_path, img_name+'_sigma_{:.3f}_process_lambda_{:.3f}_{}_psnr_{:.4f}{}'.format(noise_level_img,lambda_,current_time,psnr,ext)))
# --------------------------------
# (4) img_LEH
# --------------------------------
img_L = util.single2uint(img_L).squeeze()
if save_LEH:
k_v = k/np.max(k)*1.0
if n_channels==1:
k_v = util.single2uint(k_v)
else:
k_v = util.single2uint(np.tile(k_v[..., np.newaxis], [1, 1, n_channels]))
k_v = cv2.resize(k_v, (3*k_v.shape[1], 3*k_v.shape[0]), interpolation=cv2.INTER_NEAREST)
img_I = cv2.resize(img_L, (sf*img_L.shape[1], sf*img_L.shape[0]), interpolation=cv2.INTER_NEAREST)
img_I[:k_v.shape[0], -k_v.shape[1]:, ...] = k_v
img_I[:img_L.shape[0], :img_L.shape[1], ...] = img_L
util.imshow(np.concatenate([img_I, img_E, img_H], axis=1), title='LR / Recovered / Ground-truth') if show_img else None
util.imsave(np.concatenate([img_I, img_E, img_H], axis=1), os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index)+'_LEH'+ext))
if save_L:
util.imsave(img_L, os.path.join(E_path, img_name+'_x'+str(sf)+'_k'+str(k_index)+'_LR'+ext))
if n_channels == 3:
img_E_y = util.rgb2ycbcr(img_E, only_y=True)
img_H_y = util.rgb2ycbcr(img_H, only_y=True)
psnr_y = util.calculate_psnr(img_E_y, img_H_y, border=border)
test_results['psnr_y'].append(psnr_y)
# --------------------------------
# Average PSNR and LPIPS for all images
# --------------------------------
ave_psnr_k = sum(test_results['psnr']) / len(test_results['psnr'])
logger.info('------> Average PSNR(RGB) of ({}) scale factor: ({}), kernel: ({}) sigma: ({:.3f}): {:.4f} dB'.format(testset_name, sf, k_index, noise_level_model, ave_psnr_k))
test_results_ave['psnr_sf_k'].append(ave_psnr_k)
if n_channels == 3: # RGB image
ave_psnr_y_k = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
logger.info('------> Average PSNR(Y) of ({}) scale factor: ({}), kernel: ({}) sigma: ({:.3f}): {:.4f} dB'.format(testset_name, sf, k_index, noise_level_model, ave_psnr_y_k))
test_results_ave['psnr_y_sf_k'].append(ave_psnr_y_k)
if calc_LPIPS:
ave_lpips_k = sum(test_results['lpips']) / len(test_results['lpips'])
logger.info('------> Average LPIPS of ({}) scale factor: ({}), kernel: ({}) sigma: ({:.3f}): {:.4f}'.format(testset_name, sf, k_index, noise_level_model, ave_lpips_k))
test_results_ave['lpips'].append(ave_lpips_k)
return test_results_ave
# experiments
lambdas = [lambda_*i for i in range(2,13)]
for lambda_ in lambdas:
#for zeta_i in [zeta*i for i in range(2,4)]:
for zeta_i in [0.25]:
test_results_ave = test_rho(lambda_, zeta=zeta_i, model_output_type=model_output_type)
# ---------------------------------------
# Average PSNR and LPIPS for all sf and kernels
# ---------------------------------------
ave_psnr_sf_k = sum(test_results_ave['psnr_sf_k']) / len(test_results_ave['psnr_sf_k'])
logger.info('------> Average PSNR of ({}) {:.4f} dB'.format(testset_name, ave_psnr_sf_k))
if n_channels == 3:
ave_psnr_y_sf_k = sum(test_results_ave['psnr_y_sf_k']) / len(test_results_ave['psnr_y_sf_k'])
logger.info('------> Average PSNR-Y of ({}) {:.4f} dB'.format(testset_name, ave_psnr_y_sf_k))
if calc_LPIPS:
ave_lpips_sf_k = sum(test_results_ave['lpips']) / len(test_results_ave['lpips'])
logger.info('------> Average LPIPS of ({}) {:.4f}'.format(testset_name, ave_lpips_sf_k))
if __name__ == '__main__':
main()